Selection of transmission media in an ad-hoc network based upon approximate predicted information utility

ABSTRACT

A method of managing traffic in an ad hoc network includes measuring local data traffic levels on at least one network resource. Criteria are established for different transmission media. A microutility of a data sample is evaluated and the data sample is transmitted through one or more media, the selection of the media being based upon evaluation of the microutility against the criteria.

BACKGROUND

Vehicle Ad-Hoc Networks (VANETS) and similar networks for pedestriansand bicyclists are very different from traditional ad hoc networks. InVANETS, most network nodes are mobile, with a significant fraction oflinks breaking every second. Most nodes are both data sources(transmitters) and data sinks (receivers) and output data almostcontinuously. The nodes have information needs that are continuouslychanging based upon their position. They may be spaced far apart, aswhen they leave the dense urban cores, and sometimes operate in “urbancanyons” with very poor wireless connectivity. Network congestion, linkinstability and vehicle density vary widely and may change rapidly, aswhen a traffic light changes, releasing a queue of waiting vehicles.Network congestion, scalability issues, link instability and thecomplexity of application-building pose problems for these networks thatwould be nearly insurmountable using current techniques.

Two early large projects on large wireless ad hoc networks are CarNetand Fleetnet, discussed by R. Meier and V. Cahill, see “Exploitingproximity in event-based middleware for collaborative mobileapplications,” in Proc. Distributed Applications and InteroperableSystems: 4th IFIP WG6.1 International Conference, pages 285-296, 2003;and by H. Hartenstein et al., see “Position-aware ad hoc wirelessnetworks for inter-vehicle communications: The fleetnet project,” inProc. ACM Symposium on Mobile ad hoc networking and computing, pages259-262, 2001.

Routing protocols for VANETs have been disclosed, for example, by R.Morris et al., see “Carnet: a scalable ad hoc wireless network system,”in Proc. 9th workshop on ACM SIGOPS European Workshop, pages 61-65,2000; Hartenstein et al. (previously cited); L. Wischhof et al., see“Adaptive broadcast for travel and traffic information distributionbased on intervehicle communication,” in Proc. Intelligent VehicleSymposium, IEEE, 2003; and by J. Tian et al., see “Spatially awarepacket routing for mobile ad hoc intervehicle radio networks,” in Proc.ITSC. IEEE, 2003.

Security issues in VANETs have been disclosed, for example, by J. Hubauxet al., see “The security and privacy of smart vehicles,” in Securityand Privacy Magazine, volume 2(3); M. E. Zarki et al., see “Securityissues in a future vehicular network,” in European Wireless, 2002; P.Golle et al., see “Detecting and correcting malicious data in VANETs,”in Proc. ACM VANET '04, 2004; and control issues are discussed by M. T.J. Hedrick et al., see “Control issues in automated highway systems,” inIEEE Control Systems Magazine, volume 14(6), pages 21-32, 1994.

VANET safety applications have been disclosed, for example, L.Briesemeister et al., see “Disseminating messages among highly mobilehosts based on inter-vehicle communications,” in Proc. IEEE IntelligentVehicles Symposium, October 2000; J. Yin, et al., see “Performanceevaluation of safety applications over DSRC vehicular ad hoc networks,”in Proc. ACM VANET '04, October 2004; Route planning and transportationare discussed by J. Wahle, see “Information in intelligenttransportation systems,” in Ph.D. Thesis, January 2002.

The issue of proximity based data dissemination has been discussed (byR. Meier and V. Cahill, see “Exploiting proximity in event-basedmiddleware for collaborative mobile applications,” in Proc. DistributedApplications and Interoperable Systems: 4th IFIP WG6.1 InternationalConference, pages 285-296, 2003), teaching an architecture that allowsproximity based subscription to information based on proximity wheredifferent applications can subscribe to information within differentdistances. However, the possibility of a fractional amount ofinformation reaching a destination is not disclosed.

The idea of layered data dissemination is discussed by L. B. Michael(see “Adaptive layered data structure for inter-vehicle communication inad hoc communication networks,” in Proc. 8th World Congress onIntelligent Transportation, 2001) that was motivated by aproximity-based need for information in different applications. UsingMichael's data structure, packets are transmitted with layers of datawhich are successively removed from the data structure as theinformation travels further from the source. A disadvantage to thisapproach is that it prohibits the possibility of fractional informationof any form different from that present in the layered data structure.Moreover, neither Meier and Cahill nor Michael quantifies the need forvariable resolution information based on proximity to the source of theinformation.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an embodiment of a vehicle ad-hoc network.

FIG. 2 shows an embodiment of a node in a vehicle ad-hoc network.

FIG. 3 shows an example of an overlap region between two broadcastregions.

FIG. 4 shows an embodiment of an information layer setting utility,sending data and forwarding data.

FIG. 5 show an example of node groups for which information hasdifferent relevance.

FIG. 6 illustrates different nodes for which parking information has adifferent utility.

FIG. 7 shows a graph of an embodiment of utility for parkinginformation.

FIG. 8 shows an example of sector for data distribution.

FIG. 9 shows a graphical representation of a time-space domain fordynamic priority.

FIG. 10 shows a graph of one embodiment of a dynamic priority.

FIG. 11 shows a pattern of microutilities for quantization cells.

FIG. 12 shows an embodiment of a specific microutility as applied toquantization cells.

FIG. 13 shows an embodiment of propagation options for propagation ofinformation.

FIG. 14 shows an embodiment of an information layer with an expandedview of a forwarding sublayer.

FIG. 15 shows an example of thresholds used in congestion management.

FIG. 16 shows an embodiment of a network having groups of nodes in closeproximity.

FIG. 17 shows an embodiment of a local synopsis being compared to aremote synopsis.

FIG. 18 shows an embodiment of a local synopsis having a difference froma remote synopsis.

FIG. 19 shows a graphical representation of a time-base limbo holdinginterval.

DETAILED DESCRIPTION

An example of a VANET is shown in FIG. 1. Node 10 is a fixed node thatcommunicates with other nodes as they come into range. Nodes 12, 14 and16 are mobile nodes in various situations. As can be seen, node 12 is ina very dense environment, in close proximity with several other nodes.Node 14 is in a very sparse environment, and node 16 is on a fast-movingroad. Each of these nodes is broadcasting its information frequently.Several problems may arise in an ad hoc network having nodes that maytraverse all of these environments in a short time. Disseminating theinformation from the nodes, updating information at other nodes, anddetermining at what points the information should no longer bedisseminated for various applications would be nearly impossible withcurrent techniques.

FIG. 2 shows an embodiment of a node, such as node 12. Node 12 mayinclude a processor 13 employing a ‘protocol stack.’ Network protocolsare usually organized as stacks with higher layers passing informationfor transmission to lower layers and lower layers passing receivedinformation to the higher layers. The term ‘layer’ refers generally to alogical separation of functions. Each layer may be implemented ashardware, software or a mix of the two. In one embodiment of the node12, the processor 13 is employing a network stack that includes a newlayer, the information layer 20, interfacing with an application layer32 and the networking layer or layers 30.

One skilled in the art will understand that not all of the displayedfeatures of the node 12 need to be present for all embodiments. Such askilled person will understand that the node 12 may be a data sourcenode with a data source 18, and that the processor 13 need not be ageneral purpose processor. The device may be referred to as a computer,in that it has a processor 13. Further, nodes within the scope of theclaims may include data source nodes, recipient nodes, and relay nodes.The node may also include a port 19 for reception and transmission ofinformation.

Further, one skilled in the art will understand that a procedure orprocess may be a self-consistent sequence of computerized processes thatlead to a desired result. These processes can be defined by one or morecomputer instructions. These processes may be stored all or in part onan article of computer readable media containing the instructions. Theinstructions when executed cause the computer, here the device 10, toperform the processes set out herein.

FIG. 2 shows the protocol stack with the information layer 20 dividedinto further sublayers. Network protocols are usually organized asstacks with higher layers passing information for transmission to lowerlayers and lower layers passing up received information to the higherlayers. FIG. 2 follows this pattern in the information layer, dividingit into the sub-layers that will be described in detail in the next fewsections. The information layer provided here is built on top of astandard network protocol stack and shown as a single box 30 at thebottom of FIG. 2, although, in practice, it consists of multiplesublayers. Experiments conducted by the inventors have used a standardUDP/IP protocol stack with an 802.11b interface which is closely relatedto DSRC (802.11p). Almost any network protocol would be suitable at thislevel, including other contention-based and scheduled protocols. In thisexample, application layer 32 sits on top of the information layer.

Information dissemination is very closely related to databasesynchronization. It was found that the techniques of “epidemicalgorithms” were useful in two places in the design. First, a “gossip”mechanism is used to suppress unnecessary broadcasts at a low level ofthe system. Flooding algorithms use broadcast and rebroadcast to pushupdates effectively across large areas. This can lead to congestion, asdata is repeated unnecessarily many times. Gossip algorithms preventthis by listening to other nodes' transmissions and dropping data whichthey have overheard being successfully forwarded by other nodes. Theterm “gossip” comes from the social sciences where it was observed thatindividuals stopped spreading rumors once they had heard them severaltimes.

The system uses a form of “anti-entropy” to synchronize informationbetween moving vehicles as they encounter other vehicles which have notreceived the same set of data. The version of anti-entropy used here isutility-aware, so it synchronizes only the most valuable updates.Entropy is the increasing disorder in a physical system. The term hereis used to describe distributed algorithms that remove the disorderbetween the database copies by exchanging updates.

This system adds several innovations on top of Geocast. Geocast limitsflooding to cover a prescribed geographic area. The system uses geocastsincorporating gossip algorithms, and so the forwarding is as rapid asgeocast and prevents redundant transmissions as effectively asgossip-based methods. The microutility approach uses multiple geocasts,adjusting the geometric scope of each to deliver different quantities ofdata to different spatial regions, maximizing the quantity or frequencyof data delivery to the most relevant destinations. Moreover, thegeocasts include dynamic priority information, so that when congestionarises in the network, data can be dropped or queued in a utility-awarefashion.

In the information layer, the planning of a multiplicity of geocasts andthe setting of their dynamic priorities is accomplished by a strategycomputation that combines utility functions supplied by the applicationswith statistical information about vehicle flow patterns. This makesapplications far simpler to write, since they no longer must designtheir own geocast patterns, monitor vehicle flows and adapt theirgeocasts. Furthermore, it eliminates the complexity of applicationshaving to detect the presence of competing applications, compare theirrelative priorities and adapt their own behavior.

For example, the addition of a new high-priority application mightnecessitate redesigning the geocast patterns of all other applicationsto free sufficient guaranteed bandwidth. By contrast, the strategysublayer 22 can adapt easily and automatically to these kinds ofchanges, readjusting the geocasts to maximize the overall utility underthe current traffic constraints of the network. The system can achievemuch higher utility from this strategy computation, and yet stillbenefit from the simplicity and efficiency of geocast.

The top of FIG. 2 shows a single application 32 using the informationlayer 20, although one of the strengths of this architecture is theability of multiple applications to use a common interface. Anapplication gives the strategy sublayer 22 a very high-levelspecification of its information dissemination needs in the form of autility function. The strategy sublayer 22 also has relevance anddistance metrics, either previously provided by traffic engineers orgathered automatically from the network, describing statistical vehicleflow in each neighborhood and the predicted utility of data tohypothetical vehicles in those regions.

The information layer resides on nodes operating as data sources, onnodes operating as relay nodes and on nodes operating as recipients inthe system. A node in the system may play one or more roles with respectto a given data type: for example it may be a source of a data type, andit might also operate as a relay node, propagating the same data typereceived from its neighbors, or it may be a data sink and processreceived data in an application. The information layer 20 has a slightlydifferent function depending upon its role. For example, in the datasource role, the information layer will receive the utility functionfrom the application needing to propagate information.

The information layer 20 applies the utility function to produce amicroutility. The microutility travels with a data sample and providesguidance to the system as to how the information is to be sent andhandled through the system, such as how far and to which geographic areathe information should propagate. By contrast, in the relay role, theinformation layer does not require that the application be runninglocally, and makes propagation decisions purely based on the contents ofthe packet and contextual information about the node and itsneighborhood.

The information layer 20 applies the utility function to produce amicroutility. The microutility travels with a data sample and providesguidance to the system as to how the information is to be sent andhandled through the system, such as how far and to which geographicareas. The microutility acts as a dynamic priority, which is to say, apriority which is evaluated as the data sample moves through the networkrather than fixed at the time the data sample is created. This allowsdecisions to be made based on dynamically-changing information which isnot available to the data source node, such as the level of congestionat the location of a relay node. For example, two different nodes at twodifferent locations from the data source may evaluate the microutilityof a sample differently, one node deciding to transmit the data sampleand its microutility in the system, the other node deciding to drop thesample.

Using these mechanisms, the strategy generation sublayer 22, alsoreferred to as the microutility generation module, can send informationwhere it is most relevant. It could, for instance, send information alonger distance down roads with fast-moving vehicles, because thosevehicles may need more time to respond to data than slower movingvehicles. To prepare for information dissemination, the strategysublayer 22 develops a plan based on utility, relevance, and distancemetrics, as well as contextual information, such as its current estimateof the level of congestion in various regions of the network. When theapplication sends an individual data sample, the strategy sublayer 22assigns a microutility to the sample based on a plan devisedautomatically from the utility, relevance, and distance metrics. Therelevance and distance metrics may be previously-provided in the systemat configuration or start up, or these metrics may be gathered by thesystem. The microutility specifies, among other things, a geometricregion to which the data should be delivered; the microutility and thedata are provided to the forwarding sublayer 24.

As mentioned above, the information layer 20, including its sublayers ormodules, resides on nodes operating as data sources, relay nodes andrecipient nodes. Each of these nodes may be able to play any one ofthese roles, depending upon the situation. For example, a relay node maybe also a source node and a recipient. When operating as a relay node, anode may receive a data sample with an associated microutility, whichincludes information as to how the data sample is to be handled intransit. The relay nodes evaluate the microutility to determine how thedata sample is to be propagated through the network.

The effect of the microutility depends upon the characteristics of thenode. For example, a node close to the data source may evaluate themicroutility and determine that it needs to forward the data samplebecause the data appears to have significant remaining utility and thenode is within the geometric delivery region. A node outside theintended area may choose not to forward the data sample, as theusefulness of the data sample in the area in which the node resides iszero. The node evaluates the microutility based upon the node'scharacteristics to decide whether to further propagate the data, and howbest to do so. The microutility may be thought of as a priority, butbecause the effect of it changes based upon data available only to thenode evaluating it and the time and place of its evaluation, it is adynamic priority.

The purpose of the forwarding sublayer 24 is to send data over multiplehops in the network, if necessary, to reach the entire geometric regionspecified in the microutility. This sublayer plays a role in both theinitial transmission of data and in the retransmission of data receivedfrom neighboring nodes, as show by the data flows of FIG. 4. Theforwarding sublayer 24 also reacts quickly to local network congestionand reduces traffic load by dropping or propagating packets selectivelybased on the dynamic priorities of data samples in the system.

Once a decision is made to forward a packet, the broadcast sublayer 26handles the transmission. This layer listens for multiple broadcasts ofthe same data by other nodes, and is thereby able to reduce the numberof duplicate transmissions of the same data in the same area, a frequentproblem with flooding algorithms.

The broadcast sublayer 26 is responsible for broadcasting a data sampleand its associated microutility to the neighboring nodes. Tn a carriersense multiple access network (CSMA) this involves waiting for thechannel to be free and then sending the data sample. However, wheninformation is forwarded from node-to-node, that is, when receivers ofdata samples are retransmitting the data samples, extra care must betaken to avoid introducing too much traffic into the network. Thebroadcast sublayer 26 may address two potential problems.

The first problem may occur if a node receives an update more than once.It should not propagate the update a second time. This may be avoidedwith a table or other set maintenance technique that records recentlyreceived updates, and does not deliver duplicates to the forwardingsublayer 24. This duplicate suppression prevents circulating updatesunnecessarily in the network.

The second problem occurs in dense regions, if every recipient attemptsto forward an update once or more, then the update will getretransmitted many more times than necessary. FIG. 3 shows a potentialproblem. The intersection of circles 34 and 36 shows an overlap regionbetween broadcast regions for nodes A and E. If every node in thisFigure broadcasts an update then there will be 8 transmissions of theupdate. However, if node A broadcasts the update and then node Eretransmits the update, then every node will receive the update withmuch less network traffic. Unfortunately the solution is not usually assimple as just described.

In the information layer, gossip-based suppression is used to thin therebroadcasting of updates in areas of high density. Gossip-basedsuppression works by temporarily delaying the broadcast of an update bya random amount in an interval [0,τ]. While forwarding is temporarilydelayed, neighboring transmissions may be received, and the number ofbroadcasts of the same update during the delay are counted. If more thang transmissions are overheard, then the transmission is suppressed, asthe message has been received with high confidence by all relevantnodes.

For example, in FIG. 3, if g 3, A broadcasts the update, and then both Cand D rebroadcast the update, then the nodes B, E, F will not retransmitthe data because they have already received g=3 copies of the data. Gand H will continue to attempt to forward the update since they haveonly received 2 copies, but one of them will suppress the other. In thiscase, gossip suppression does not find the minimal solution torebroadcasting the update, which was 2. Nevertheless gossip suppressionis a simple technique that greatly reduces the amount of dataretransmission in dense networks.

One skilled in the art will recognize that many variations on mechanismsthat suppress data retransmissions on data received exist and may beused in place of any specific mechanisms set forth here. No limitationto any particular suppression mechanism is intended nor should beimplied here.

Finally the congestion sublayer makes no modifications to the content ofthe data packets, but adds a congestion report to the headers of packetsbefore they are transmitted, collects these reports from receivedpackets, and produces a congestion summary to inform the forwarding andstrategy sublayers of the local network congestion. The informationlayer may then decide to drop data samples or select alternative mediafor transmission of the data samples.

Congestion reports may also be included in explicit management packets,rather than included in data packets. The congestion summary may be foran area rather than specific nodes and may be of an extended view, suchas system wide, rather than local. The information layer may then decideto drop data samples or select alternative media for transmission of thedata samples.

FIG. 4 illustrates the operation of the system. At 38, an applicationinitially supplies a utility function and the strategy sublayer does thecomputations necessary to generate individual microutilities. Thesecomputations may be performed in advance, periodically, or at the timeof the data generation. At 40, when the application generates anindividual data sample the strategy sublayer assigns a microutility and,if appropriate, begins transmitting the data sample. At 42, nodesforward the data sample according to the microutility. The dashed arrowshows the possibility of a node's application receiving the data.

While 38 is very efficient—it should be possible for an application tochange utility functions in a few seconds—40 and 42 are extraordinarilyefficient. The most frequently performed operation of the system 42,forwarding of data, in most cases requires only simple geometric testsand the evaluation of linear functions. It is believed that theseoperations are simple enough to be performed, at packet rates, by even arelatively small microcontroller.

The system is designed so that utility functions supplied in 38, whilenot directly part of the frequently executed processes 40 and 42, aredistilled into simple microutilities that influence the informationpropagation of the system. These microutilities also guide two otherimportant operations of the protocol not shown in FIG. 4. Some of thedata of high utility is sent by longer range channels, and data withutility persisting over a longer time period is stored temporarily intransit, for later forwarding after vehicles have moved andcommunication possibilities have changed. Altogether microutility isused extensively to maximize the utilization of the network.

Utility

A utility function describes how valuable data delivery is torecipients. It can assign value to such things as: how much data isdelivered and the timeliness of delivery. The utility functions are whateconomics would call “cardinal utility” functions: their magnitude isimportant: data with utility 10 is twice as valuable as data withutility 5. It is intuitively useful to think of utility as the amountthat a recipient would pay for the data delivery (e.g. $10). However, ina typical market the monetary amounts necessary to purchase goods do notnecessarily agree with the utility of those goods. The system treatsutility as a unitless quantitative measure of satisfaction, although amonetary interpretation would still be appropriate for what follows. Inany case, utility serves as a medium of exchange with which theapplications may compete for limited bandwidth in different parts of thenetwork.

The system assumes that there are sources generating periodic streams ofsampled data and that an essential operation of the system is droppingsamples from these streams. It is further assumed that a downsampledstream of data will have less information content and that utility forinformation will usually diminish with distance from the source. It isthis diminishing utility that will allow the system to drop data as itpropagates further from the source, thereby reducing data traffic.

The information disseminated in the system has a utility functionapplied to it. The utility function determines the propagation of theinformation. Generally, the utility function will be based upon anapplication for which the information is intended when transmitted froma data source. The utility function may be altered to reflect localconditions. The utility function supplied by the application maytherefore be referred to as the generic utility function. Genericutility functions, after being altered by local conditions, may bereferred to as specific utility functions. This discussion will use theunqualified term utility function to refer to either the generic utilityfunction or the specific utility function.

The system is architected around the general concept of each applicationproviding a utility function for information (denoted generally by I):

U(I, {right arrow over (x)}_(t), {right arrow over (X)}_(s)), where therecipient is located at geographic location {right arrow over (x)}_(t)and the data source is located at {right arrow over (x)}_(s). In ourscenarios, this is often reduced to the case U(I, {right arrow over(x)}) where {right arrow over (x)}={right arrow over (x)}_(t)−{rightarrow over (x)}_(s,).

Rather than using an abstract information content I, the system willwork with a more concrete special case, where the system assume that thesource is generating regular data samples and that information contentis reduced by dropping a subset of the data samples either immediatelyat the source or in-transit when congestion arises. This is typical ofmany applications. For example, if the source is sending position andvelocity of vehicles, then discarding data samples will reduce theaccuracy of predicted vehicle locations. It is possible to simplify theparameterization of U by replacing the abstract representation ofinformation, I, with the average frequency of data received, f, and thetypical delay τ of data samples received. These two parameters areconnected with information: less frequent updates received from asource, or more delayed updates from a source, will mean that the systemhas less information about the current state of the source:

U(f, τ, {right arrow over (x)}).

However, rather than geographic displacement x, the utility function maybe based on a distance metric d that is based on the typical time forthe vehicle receiving the information to arrive at the informationsource. The metric d({right arrow over (x)}_(t), {right arrow over(x)}_(s)) will capture the salient features of road geometry and flowrates. For example, d will capture the direction of travel of thereceiving vehicle; if the vehicle is already traveling towards thesource, then the distance to the source will be less:

U(f, τ, d).

A key operation of the system will be adjusting f over space to maximizeutility while minimizing cost of propagation. More specifically, havingf decrease with distance from the data sources will reduce networktraffic. In certain applications, data available at the source (e.g. thenumber of spaces available in a parking garage or the color of a trafficlight), will affect the region, distance, time or characteristics of thevehicles for which the data has high utility. For example, frequentupdates about a filling parking lot which is nearly-full is of highvalue to nearby cars, since they would want to know of a closure, but oflittle use to far-away cars, since the lot is almost certain to be fullby the time they arrive. Since the microutilities are preplanned at thesource, they can easily be adjusted according to the source conditions.

One skilled in the art will recognize that U may depend upon othercharacteristics of the data delivery. No limitation to any particularcharacteristics is intended nor should be implied by any examples givenhere.

As described above, generic utility functions are determined by theapplication writer when an application is created or installed on thedata source nodes. Some factors that may affect utility include whetherthe vehicle will receive information within a certain time, the rate atwhich uncertainty of the measured quantity grows, whether the vehiclewill make a decision based on the received information, the accuracy ofthe received information, and how much was “at stake” when decisions aremade based on the data.

While all of these factors play a role in the utility, some are easierto model than others. The information layer can provide criteria to anapplication to help it make a decision. It could, for instance, providecriteria based on how many cars are likely to be present at a particularlocation, how many cars are driving in particularly relevant direction,or are likely to pass by some other location, such as a parking lot, orpass through a region with available traffic information. For thisreason, the system will factor utility into two parts:

U(f, τ, d)r({right arrow over (x)}_(t), {right arrow over (x)}_(s))

where U is the expected utility per-vehicle receiving the information,and, r({right arrow over (x)}_(t), {right arrow over (x)}_(s)), whichthe system will call “relevance,” is a prediction of the importance of adata sample to vehicles near {right arrow over (x)}_(t) that will findthe information from the source {right arrow over (x)}_(s) useful. Thesystem expects the application writer to supply U and in some casesr({right arrow over (x)}_(t), {right arrow over (x)}_(s)). However, theinformation layer can also provide a standard r({right arrow over(x)}_(t), {right arrow over (x)}_(s)) based on gathering data on wherevehicles travel. This could be, for example, a selective traffic modelbased on sampling the trajectories of vehicles currently at the locationof interest and using this data to estimate the probability that theywill travel near the source.

When r({right arrow over (x)}_(t), {right arrow over (x)}_(s)) is anonuniform distribution, the information layer can save bandwidth bydirecting information to where it is most useful. Systems with largenumbers of participants with rapidly changing positions, such as vehiclenetworks, are often architected as “push” systems, in which data istransmitted to destinations without first receiving explicit requests.Metrics such as relevance allow these “push” architectures to directinformation to where it is statistically most likely to be needed usingthe relevance function r({right arrow over (x)}_(t), {right arrow over(x)}_(s)) and without requiring any communication with potentialrecipients.

FIG. 5 shows an example of a relevance metric near a traffic light. Forexample, in FIG. 5, the information propagated from the traffic lighthas low relevance for group 44, with a long distance associated with anypotential use of the data to those recipients. The potential recipientsin group 44 would have to go around the block to visit the trafficlight, and are heading away from the light. The information has highrelevance for the recipients in group 48, as those recipients areheading towards the light and have a high probability of actuallyvisiting the area controlled by the light. Recipients in group 46 wouldprobably consider the data highly relevant, the distance is short, andthe vehicles are moving fast. These factors are considered in therelevance metric.

The accuracy of information is a common trait to most applications andprobably most easily modeled. Most decisions involve predicting thestate of future events (e.g. traffic jams, parking availability, etc.),but the predictive accuracy of information will decrease withinformation age. This often sets up a natural decay in the value ofinformation with distance. and time. Knowing, for example, that aparking garage is full will be less valuable to vehicles further awayfrom the garage because, in the time it takes to drive to the garage,the information may become obsolete. This decay in value with distanceand time will prove critical to reducing the network traffic.

The potential loss or benefit of a type of information is much moreapplication specific. Understanding loss or benefit will help prioritizeapplications with respect to each other, and packets relative to oneanother within a single application. For example, information thatsupports a safety application may have much higher utility than parkinginformation, while vehicle information describing a lane blockage ismore important than vehicle information describing a smooth flow. Theloss or benefit often provides an application-specific variation inutility with distance. For example, parking garage information will bemost useful at moderate distances, where this information is stilllikely to be applicable at a near-future time when a vehicle arrives atthe garage, but where alternate routes can still be taken to othergarages if the garage in question appears likely to fill up.

As mentioned above, the application writer will supply the genericutility function governing the propagation of information for theapplication. This is different from a typical economic model whereindividual recipients and individual data sources have their own uniqueutility for data delivery and a market mechanism is used to optimizeresources across these utilities. Instead, the push architecture, whichinherently delivers benefits of many recipients at the same time, isbetter served if the application writer supplies a generic utilityfunction that estimates the benefits of all the likely recipients of theinformation propagation. When more than one application has use for thesame data, then both applications can supply generic utility functionsand these can be combined to determine a generic utility function forthe data type.

This makes deriving generic utility functions, especially accounting forthe factors affecting utility described above, a challenging task.Fortunately, utilities will be derived infrequently, and by a smallnumber of application writers. Moreover, these utilities should not beover-designed for accuracy. The choice of the form of utility in thetwo-part equation limits the dependencies of the utility to parameterssuch as frequency of delivery, delay, traffic flow speed, etc., andthese dependencies will not cover all the factors affecting utility forapplications. Perfect modeling of utility is not possible. Only thoseparameters that might significantly affect utility are chosen, and someloss in accuracy will be accepted. Likewise, application writers shouldchoose only the most salient features of utility to model theirapplication.

Utility Specification

To specify utility, the application writer may identify a set ofpossible information delivery patterns for the data samples at a datasource. As this system is generally a push architecture, the applicationmust project and predict the possible delivery patterns that may occurat locations in the system and specify utility for each of the set ofpossible delivery patterns. Further, there may or may not be recipientsat those locations. The probability of there being a recipient at aspecified location becomes a factor to be accounted for in the possibledelivery patterns, as does a probability that a receiving node locatedat the specified location will be interested in the information andobserved characteristics of the delivery patterns observed by areceiving node located at a specified location.

The generic utility function predicts the usefulness of the informationto the recipients and is used at the source, rather than at therecipient. As described above, several factors affect utility, includingfrequency of updates, the delay of those updates in reaching therecipient, the direction-of-travel of the recipient and the distance ofthe recipient from the source. These can be more generally referred toas time and position factors.

Time factors would include frequency, or how far apart in time thesamples are sent, and delay, or how far apart in time from transmission,the samples are received. Position factors would include the distancebetween the source and the recipient, both with regard to the physicaldistance, and how the distance affects the time of reception. Positionfactors would also include the direction of movement between the sourceand the recipient. The time and position factors may combine together,such as discussed above with regard to adjusting the frequency overspace to maximize utility.

The generic utility function may then be used to guide the propagationof the data sample in the network. Guiding the data sample may includesuch tasks as making choices between alternative media for transmission,deciding at what point in the network the samples should be dropped,re-ordering transmission queues in which the data samples are waitingfor transmission, and deciding whether or not to hold a data sample forfurther transmission. Re-ordering a transmission queue may be referredto as modifying a temporal order of transmission of the data sample orsamples.

In one example of a generic utility function, a parking lot applicationis assumed in which parking lots send information about their currentoccupancy as well as information such as arrival and departure ratesthat will allow recipients to predict future occupancy. This informationcan be used by vehicles in the vicinity to plan their parkingdestination.

FIG. 6 illustrates the utility of parking lot information. The utilityhas the highest value at medium distances such as at 50, where theinformation is reasonably predictive of future occupancy and a drivercan easily divert to alternative garages. At larger distances such as at52, the information loses utility because it is less predictive offuture occupancy and choosing a specific garage is unnecessary. Atcloser range, rapid updates are helpful, since the driver can takeadvantage of even a single space opening up. Overall, the driver hasless to gain up close as the driver has already driven into closeproximity of the garage, although it may have some value in areas withmany garages which the driver must choose between.

FIG. 7 shows a sample utility function for parking lot information. Theutility function shows the highest utility at moderate distances fromthe garage. The utility function also shows the steepest slope at lowvalues of f, and shows diminishing value for larger values of f. Thisdiminishing value is expected. Very frequent reports from the garagewill not significantly improve the prediction of future occupancy.

In this example, the parking lot utility function depends on the stateof the garage through the parameters of K, current free space, λ_(in),rate of arrivals, and λ_(out), rate of departures. In particular, whenthe garage is about to become full, the shape of FIG. 7 will shifttowards the origin, indicating that the information about the garagewill be most useful to those vehicles near the garage that still mightarrive before the garage fills. Vehicles further away will know withmore certainty that the garage will fill and will not require frequentupdates. This illustrates a feature of the utility function; it candepend upon the data at the source. Because of this feature, the systemdescribed later can expand and contract the distance over which the datais sent based on the state of the garage.

With regard to the variables, the utility function of FIG. 7 is derivedas follows. T_(α) is the expected time of arrival at the garage, f thefrequency of delivery is the product of a base rate f₀ and a randomizeddownsampling factor γ. Randomized downsampling as used here means thaton the average γ percent of the original samples are received, theothers being proactively or reactively dropped in transit. In thisdefinition, f₀ is the regular frequency while f is an average frequency.Utility is defined as the product of three parts.

The first part is the probability of receiving an update, and is definedas:

P_(γ)=1−(1−γ)^(f) ⁰ ^(T) ^(α) since (1−γ)^(f) ₀ ^(T) _(α) is theprobability that no message is received by T_(α).

The second part is the probability that received message is correct bythe time of arrival. For the purpose of this example, ground truth ismodeled by a Skellam distribution, which is roughly Gaussian.

$P_{c} = {{\int_{x > 0}{{p_{GT}(x)}{x}}} = {1 - {Q\left( \frac{K + {\left( {\lambda_{out} - \lambda_{i\; n}} \right)T_{\alpha}}}{\sqrt{\left( {\lambda_{out} + \lambda_{i\; n}} \right)T_{\alpha}}} \right)}}}$

The third part is the potential benefit of the received message. This ismodeled by an exponential distribution that corresponds to a Poissondistribution of alternative garages:

de^(−βd).

The resulting utility function is shown in FIG. 7.

Notice that this utility function contains considerable informationabout utility. The most obvious feature is that a high degree of utilitycan be archived by delivering 0.2 updates per second at a distance of120 meters (this is the “knee” of the function). But the functioncontains additional information: at 200 meters the there is also someutility for information delivery, although considerably less utility.And at 120 meters there is very little utility to be gained byincreasing the utility beyond 0.2 updates-per-second, the functionbecomes nearly flat. This additional information can exploited to guideinformation propagation, for example, if additional bandwidth becomesavailable, this utility function indicates that it would be better tosend updates further distances than to send more frequent updates to 120meters.

The system does not necessarily expect application writers to derivetheir own utility functions. It is possible to provide utilitytemplates. The idea is that the application writer would pick thetemplate with the most similarity to the application, and adjust a fewparameters to obtain a utility function. Given that even imperfectutility functions can yield large benefits, the system expects that arelatively small number of such template utilities will be sufficient tocover most future applications.

Utility allows trading off resources between multiple applications, butthis requires a good understanding of the relative importance ofdifferent applications. As time passes, resources will need to bedivided among an increasing number of applications, requiringnormalization of utilities across applications. The system is designedto envision that diverse applications will assign utilities usingdiverse scales, so that some centralized calibration of scales will benecessary. For now, centrally-managed scaling factors will be used toscale the utility for each application. This allows for easy integrationof new applications by setting a single parameter; the applicationswould share the same propagation machinery so there would be no need toengineer how a new application's propagation machinery interacts with anold application's propagation machinery.

Some normalizing operations will remain the responsibility of theapplication writer. The information layer will provide data, n({rightarrow over (x)}_(s)), on the number of nearby data sources. Theapplication may define a scaling of utility based on n({right arrow over(x)}_(s)) to discount utility when there are many nearby replicas andtherefore presumably many sources of similar information.

Similarly the information layer will provide relevance data, r({rightarrow over (x)}_(t), {right arrow over (x)}_(s)), that an applicationcan use to discount the utility of information sent to more denselypacked sinks.

The utility functions supplied by application designers can include theeffects of delay on the utility of the data received. However, it isoften easiest for the application designers to first develop utilityfunctions assuming that there are no significant delays. And then, as asubsequent step, add the time (delay) dimension to the utility function.This may be implemented as templates from which a designer can pick,based upon the way the designer would like to include the effects ofdelay in the utility function.

As one example of how delay might be added to a utility function, alinear decay of utility with time may be specified. The utility designerneed only provide one parameter: the rate of loss of utility with timeL. Then a utility function U(f,τ,d) can be derived from a utility thatassumes no delay (τ=0) by the formula: U(f,τ,d)=Max(0, U(f,0,d)−Lτ). Asanother example, an exponential decay of utility with time can be addedto a utility function by specifying a parameter quantifying the amountof time for an update to lose half of its utility value, that is, ahalf-life parameter H. Then utility can be derived by the formula:

U(f,τ,d)=U(f,0,d)(½)̂(τ/H).

As another example of how delay might be added to a utility function, atransformation may be applied to a utility function that does notcontain a time (delay) dimension. For example, if utility is based onthe average delay of information in-hand at the recipient, atransformation can be based on the relationship between the frequency ofupdates received and the average delay of the information in-hand at therecipient. For example, the no-delay form for information received at afixed frequency f implies an average delay for information in-handbetween received updates of 1/(2f). This provides a heuristicrelationship between frequency of delivery and delay, τ˜1/(2f), that canbe used to transform a utility without delay into a utility with delay:U(f, τ,d)=U(1/(1/f+2τ),0,d).

As another example of how delay might be added to a utility function, adistance transformation is described that is appropriate only forutilities that were designed based on vehicles receiving information,acting on that information, traveling to the source, and assuming thatthe value of the information when it is received depends upon the chancethat it will be valid at the future time of arrival at the destination.In this heuristic, the delay is converted to additional travel distancebefore testing the validity, and requires the parameter, Va, of thevelocity of vehicles approaching this area: U(f,τ,d)=U(f,0,d+Vaτ)

In the above parking lot example, the specification of a function offrequency of delivery of information from a data source to adestination, parameterized by such things as distance, direction andtime from the data source may be used to adapt utility. Some or all ofthese may be included in the utility function. As mentioned above, thesystem may provide utility templates to the application writer. Thesystem may allow the application writer to adjust a few parameters.These specified, adjustable parameters may be tuned based uponcontextual information, such as traffic flow density, demographicprofile of the vehicles in the system, historic knowledge as well asothers.

The utility specification may also accommodate some randomizeddownsampling of the data samples. Utility may be refined based upon adesired accuracy and desired operating points. These and other criteriamay be converted to specifications of utility based upon the frequencyof delivery.

Further, measures of the frequency of updates delivered and the delay indelivery of updates may weight more recently received updates moreheavily than older received updates. Other measures to characterizedelivery patterns that affect the utility specification may include avariance in delivery of updates, and accuracy with which a data sourcecan be estimated from data received.

Context

Further, the utility function, while specified by developers, may beadapted based upon context, where the context is the environment of aspecific data source, resulting in a specific utility function. Thefactors n({right arrow over (x)}_(s)), and r({right arrow over (x)}_(t),{right arrow over (x)}_(s)), described above, are examples of context.These and other kinds of context may be derived from such things as avehicle traffic flow model for vehicles that travel near the data sourceand the number of data sources at nearby locations.

As discussed with regard to FIG. 5, the relevance of the metrics mayimpact utility. The vehicle traffic flow model can include such thingsas vehicle density, which may be used to derive relevance of a givendata source's sample, with the relevance being lower if the density ishigher. The vehicle traffic models may also provide vehicle speeds toderive time-to-arrive metrics. This context information may be combinedwith the generic utility specification by scaling the utility function,or modifying the dependent variable input or parameter to the utilityfunction, resulting in a specific utility function.

In one embodiment of modification of the utility function, a parameterthat is part of the utility function may be modified. This modificationmay be provided for by the application writer that supplied the genericutility function. The application writer may have provided a ‘hook’ orother indicator that certain parameters of the utility function may bemodified to reflect the current environment of the data source, as anexample. An example of these parameters may include a traffic flow ofdata recipients in the environment by gathering the information from theother recipients or based on previously-provided information; in eithercase the information could be current or historical. Other examplesinclude the number of data sources in the area and the number of datasamples being propagated by the current data source.

Microutility

A microutility is associated with each individual data sample. Itspurpose is to very efficiently and very compactly capture a small aspectof the utility function. The strategy sublayer of the protocol stackwill assign microutilities to the data samples. The microutilityrepresents a part of the “plan” to realize the best utility possible forthe system; it specifies how far an individual data sample should bepropagated and how the data sample should be handled in transit.

A microutility assigns a dynamically changing priority to eachindividual data sample in transit. This is referred to as a microutilitybecause each individual data sample is given its own utility value, andthese values are used to make in transit decisions. If there iscongestion, low value items are dropped. If there is a superior (morereliable, faster, etc.) transmission medium available, high value itemswill also sent via that medium. If items are stored for futuretransmission, high value items will get preferred storage andretransmission.

The microutilities are a key simplifying element of the system. They canbe compactly represented, so they can travel with every data sample, andthey are simple to evaluate (a linear function). Any relay node, eventhose equipped with little memory or processing power, can evaluate themicroutility. It is also unnecessary that the intermediate nodes beaware of the application. All of the required information necessary forforwarding is encapsulated in the microutility.

However, this simplicity is achieved at the cost of some accuracy.Microutilities only approximate the full utility functions describedabove. The inaccuracy comes primarily from assigning microutilities toindividual data samples in isolation. The utility functions assignutility to various rates of delivery of multiple data samples, so theutility of receiving an individual data sample will usually depend onhow recently other data samples have been received from the same source.Nevertheless, the system has defined microutilities to evaluate eachdata sample individually. This simplifies in-transit processing: thereis no need to keep track of previous data samples and no need toevaluate more complex utility functions at intermediate nodes.

This simplification can still show good aggregate behavior. By assigningeach data sample from a same source a different microutility, theaggregate behavior or propagation characteristics of all of themicroutilities can be made to approximate the full utility function.This may be referred to as dithering, similar to dithering in digitalimaging, where varying numbers of picture elements (pixels) in a regionare given particular colors. When viewed with the human eye, the colorsare integrated into a color that was not available in the color paletteof the rending system. Similarly, by assigning or dithering themicroutility of several samples from the same source, the aggregateresult is a desired and closer approximation of a full utility functionthan could be achieved by a single sample. The next section, on thestrategy sublayer, describes how to assign a mixture of microutilitiesto get near-optimal group behavior.

A single microutility contains an origin, a geometric domain, and adynamic priority. The origin is the coordinates of the source of thedata. It is used in the microutility to define the geometric domain. Itis also likely to be useful to the application as the location of thedata sample, so it will also be delivered to the application layer. Whena data sample is received at the Strategy layer from an application, theutility of the data sample is converted to a microutility. As mentionedpreviously, it is possible to modify the utility of a data sample at thedata source prior to microutility creation. The resulting microutility,whether based upon an unmodified or modified utility, is in turn encodedwith the data sample to form an encoded data sample. The encoded datasample is the data sample with its accompanying microutility. Theencoded data sample is then propagated through the network based uponits microutility.

The geometric domain is the target area for data distribution. This willbe an easily-encoded and easily-tested geometric region of the plane.The exact library of geometric shapes remains to be determined; here thesystem uses a sector centered at the origin, with two rays θ1 and θ2.The system includes a tolerance w to allow extra flexibility inforwarding near the sector. As defined, the sector extends to infinity,the dynamic priority will limit the distance updates can travel from theorigin, and thereby close the infinite sector into a wedge. A typicalsector is shown in FIG. 8. In the special case θ1=θ2 a large w can beused so that sector covers a rectangular region enclosing the origin.

The sector can be represented compactly using rays to define the sector.The rays are defined as slopes, ray1=(Δx1, Δy1) and ray2=(Δx2, Δy2).Sector tests computations to determine if the sample is within itssector can be done efficiently using cross points into the slopes.Representation of the origin requires 128 bits (2×64), which are alreadyassociated with data samples. Sector representation only adds 40 bits,with 2×8 bits for each slope and 8 bits for margin. It is also possiblethat further compression may be possible, such as by encoding commonlyused geometric shapes by a well-known identifier. The microutility atpoints outside the sector is zero, within the sector the microutility isdetermined by the dynamic priority.

Dynamic priority is a linear function of two variables: the time elapsedsince the data sample was generated, and distance the data sample hastraveled since it was generated. If the (time, space) is within thetime-space domain, in this example the quadrilateral shown in FIG. 9,then the priority is defined by a linear function on this domain. Thelinear function is determined by three intercepts: Uorigin at (0, 0),Utime at (Tmax, 0), and Udistance at (0,Dmax). Outside the quadrilateralthe microutility is zero, and within the quadrilateral the linearfunction is clipped if necessary so that it will never be less thanzero. An example graph of dynamic priority is shown in FIG. 10.

The microutilities play both a proactive and a reactive role. Undernormal operation, for example, when data samples are spreading rapidlyand effectively, the microutility plays a proactive role. A data samplewill be geocast throughout the geometric domain defined in themicroutility, with the distance from the origin limited by Dmax.However, in many cases the geometric domain, particularly Dmax, will bechanged with every sample from the source, so unlike a typical geocast,the density of samples received will decay with distance. In this waythe microutilities are used proactively to deliver the optimal amountsof data at different distances.

There are also reactive uses of the microutilities: for example, whenunexpected congestion arises, the dynamic priority is evaluated and usedto discard the least valuable data samples. Unlike the full utilityfunctions, this is a simple linear function that is easy to compute andis only meant to approximate the marginal distribution of the fullutility function. It operates like a priority, but unlike typical fixedpriorities, it varies with distance and time since origination. Eachdata sample has a different dynamic priority, so that when congestionarises some, but not all samples are discarded.

Each data sample may have one or more microutilities associated with it.During evaluation of the microutility at the nodes, all of themicroutilities may be evaluated and a microutility with a maximum valuemay be used. Each microutility associated with a data sample may havedifferent geographic regions, maximum distances, time dependencies orother characteristics that differ between them.

In a system with multiple microutilities per data sample, themicroutilities may be combined in a variety of ways. One method is touse a maximum function, as described above. Another method is to use asumming function, where an evaluated microutility at a location is thesum of zero or more of the provided microutilities at that location,depending upon the characteristics of each microutility specification.

Microutilities may also be compressed or abbreviated. For example, theremay be several well-known microutilities that could be referenced by aname, number or other identifier, rather than a full specification in adata sample. This is discussed herein for geometric sectors, with theunderstanding that it could be applied to the entire microutility.

Microutilities may also employ a virtual source location and time,providing a fictitious location and time to represent some event. Forexample, a vehicle may sense ice on the road, and the source of the datareport would be the ice, not the location of the vehicle when ittransmits, which may be past the location of the ice. Alternatively,Variable Message Signage (VMS) may be placed some distance from a worksite and originate safety messages about the work site using the worksite location and time. In vehicle networks, some vehicles may not havecommunications equipment, so a ‘smart’ vehicle could send an event for a‘dumb’ vehicle and send an update on the dumb vehicle's behalf.

Similarly, the microutility may be a function of time-of-the-day. Forinstance, one may release a “persistent” data sample in a ITS networkand make it virtually reside in multiple regions and give it differentdissemination utilities at different time of the day, or even differenttime of the week, of the month, of the year, etc.

Microutilities may be authenticated via a security procedure. Theprocedure may confirm that the microutility is genuine and may include acorresponding certification that the origin node is authorized to usethe microutility in the network. One such example could be for a centralauthority to issue an application a certificate for the application'sutility function and scaling factor. The application would present thatcertificate to the strategy sublayer along with the utility function andprovide a microutility signing function by which each generatedmicroutility may be signed by the certificate holder. Receiving nodesmay verify that generated traffic conforms over time to the loadrepresented by the certificate. Nodes may also use a reputation systemto disallow traffic from applications violating their provisionedtraffic node.

One skilled in the art will recognize that microutility may include anyfunction that encodes spatially and temporally dependent information todetermine and prioritize retransmission/forwarding of the data sample.No limitation is intended nor should it be implied by any particularexample given here.

Converting from Utility to Microutility

The strategy sublayer plays the crucial role of translating the moreexpressive utility functions to the more practical microutilityfunctions. As explained earlier, it uses a dithering approach, whereeach data sample will receive a different microutility, but groups ofmicroutilities will approximate the full utility. To do this thestrategy sublayer precomputes a set of microutilities that will optimizethe utility function supplied by the application based on the currentlyavailable network resources. Note that strategy generation only needs totake place at the data source, and can take place periodically andwithout the real-time constraints the system.

Under most operating conditions, the forwarding operation does not needto generate a new microutility, only evaluate the microutility travelingwith the data sample. In some rare circumstances, a forwarding node mayrevise the microutility when it determines that the assumptions of itscreation at the source have changed. This revision may be accomplishedby modifying the utility of the data sample, which in turn has an effecton the microutility. However, this is generally not expected to occur inmost application scenarios, since the microutility generated at thesource already contains all of the information necessary to allow aforwarding node to react properly to unforeseen congestion.

In one embodiment of a strategy generation, the strategy sublayerprepares to generate individual microutilities with the followingpre-computation steps: sector choice, traffic normalization andquantization of distance, and quantization of utility. Sector choice isbased on the relevance function r({right arrow over (x)}_(t), {rightarrow over (x)}_(s)), which is either a local traffic flow model, or analternative function supplied by the application writer. The sectorchoice algorithm tries to identify sectors with a usually highrelevance. If no such sectors can be identified then updates will besent omni-directionally, that is, with a sector of 360 degrees. Evenwhen sectors have been identified, it is often useful to send someomni-directional updates to cover lower relevance areas.

Sectors are found by first computing relevance as a function of anglearound the source, r(θ). Then locally maximal peaks in r(θ) areselected, provided they exceed a predetermined threshold. Peaks areexpanded to full sectors by including adjacent values of θ for whichr(θ) still exceeds a second, smaller threshold. In some instances, theexpanding peaks may merge to form larger sectors. Once the sectors areidentified, the steps in the next few sections are performed for eachsector.

Once a sector is chosen, the system chooses a sequence of radialdistances z1, z2, z3, . . . , zn so that the traffic necessary to spreadupdates in the sector between each of the zj and zj+1 are nearly equal.If the angle of the sector θ is large, the system would expect spacingof zj to decrease with distance from the origin to compensate for theincreasing length of the arc at zj.

It is also possible to quantize the frequency of updates received, f,uniformly into 0, f1, f2, f3 . . . , fm, where fm is chosen large enoughto be in the region where the utility is nearly flat (so there is noreason to send more frequently than fm) The utility function U isbucketed into an array Ui,j by integrating (approximately if necessary)the two-part utility equation between zj−1 and zj in the sector. Uij isthe utility of delivering updates at frequency fi to the sector betweenzj−1 and zj.

One can compute the marginal of U with respect to f:

Ûi,j=Ui,j−Ui−1,j

The marginal Û can be interpreted as the additional benefit of addingmore data delivery at distance zj−1 through zj. The marginal utilitywill play a central role in the optimization problem. Intuitively, it isbest to deliver data where the marginal utility is highest, and when adelivery plan is optimal the marginal utilities of all possible“improvements” should be roughly equal.

A cost C* is determined for the bandwidth needed by each “cell” in thequantization of preceding sections. The same cost can be used for eachcell because the traffic was normalized and frequencies were evenlyspaced. In normal economic systems cost would be determined by a market.To simplify the system a cost setting mechanism may be employed. For agiven cost, c, the strategy sublayer will compute a pattern ofmicroutilities that will cover some of the cells Ûi,j shown in FIG. 11.This discussion refers to the number of cells covered as d(c), thedemand at c. The discussion of the computation of d(c) and will bedeferred in order to first describe the determination of C* given d(c).

When provided with a new utility function, the strategy sublayercomputes strategies for a regularly spaced set of thresholds c. Theseregularly spaced thresholds approximate a demand curve d(c), which isthe number of cells, or amount of network traffic, that will berequested for a given cost. Based upon this, it is possible to selectfrom different methods to set the cost C* or marginal utility.

Using basic split cost, it is assumed that all of the neighbors areidentical data sources. The cost splits the traffic evenly among nneighbors. All neighbors are assumed to have identical demand curves andwill arrive at identical cost.

The basic split cost may have some limitation when the neighbors havedifferent information sources with different utility functions andconsequentially different demand curves. When different nodes havedifferent costs, it is possible to reconcile the costs in the localneighborhood. Each node computes its own cost based upon its own demandcurve. It also computes a local weight based upon the slope of itsdemand curve at the cost computed just previously. This pair of cost andweight is communicated within the local neighborhood.

Each node can now compute a new cost by a weighted average. The new costis a sum of each weight multiplied by each cost, divided by the sum ofeach weight. This computation approximates the ‘equilibrium’ cost that amarket might reach by assuming that the demand curves are linear nearthe equilibrium point. However, unlike a market, it has the advantagethat it is reached immediately via the weighted average computation. Anadditional refinement may be available if there is some “spillover” ofinformation into neighboring regions due to the longer distancemicroutilities generated in the strategies. It is possible to propagatea portion of the local cost to neighboring regions to influence thespillover of traffic.

This cost is an internal variable of the system, it will not necessarilybe charged to users of the system. However, this cost may be a usefulinput in a subsequent procedure to determine charges to users of thesystem.

Once a cost C* is determined, strategy generation can be viewed as aneconomic choice: choose a set of cells so that the utility is greaterthan the cost. The cells must be chosen to form a convex region, asshown for example, in FIG. 1, so that they can be covered by a patternof microutilities. If the marginal utilities Ûi,j; are decreasing inboth i and j, as illustrated in FIG. 1, then the economic choice issimple: choose the cells in Û where the marginal utility is greater thanthe cost. This situation will be considered first.

FIG. 11 illustrates Ûi,j; the bold line enclosed region encloses allcells above the cost C*=32. The enclosed region can be interpreted assuggesting that the best way for this application to spend 18 “units” ofbandwidth is to send 80% of its data samples as far as z1, 60% as far asz4, 40% as far as z6, 20% as far as z7, and nothing beyond z7.

In preparation for generating individual microutilities, the systemcomputes the length of the horizontal rows that are above the costthreshold. Define g(i) for each i as the largest j such that Ûi,j isstill greater than cost C*. For FIG. 11, g(1)=7, g(2)=6, g(3)=4, g(5)=1,g(6)=0. The geometric domain of the microutilities will be chosenuniformly from the zg(i). That is, the planned distance of travel for adata sample with microutility i will be D(i) max=zg(i). The sequencesÛi,1, Ûi,2, Ûi,3, . . . , Ûi, g(i), indexed by i, will together tile thelower left corner of Ûi,j, covering all the cells with marginal utilitygreater than cost C*. FIG. 12 shows the microutilities as the horizontalrectangles 60, 62, 64 and 66.

If the marginal utilities do not decrease in i and j then the procedureto choose cells must be modified. It will be understood that for anyvalue of C, that a shortest path or dynamic programming algorithm can beused to find a sequence of values g(1), g(2), g(3) that, for the regionleft of the g(i), maximize the utility of cells minus the cost of thecells. Such an algorithm does not require that the careful choice of thezj to make the costs uniform per cell, as it can determine a cost andutility for each possible strip g(i). The relationship between utilityand cost of bandwidth can be used to determine a pattern ofmicroutilities such that a sum of the utility minus the cost ofbandwidth is increased when comparing patterns of microutilities.

Choosing zg(i) uniformly in i is the generally surest way that theforwarding sublayer delivers the most useful information to vehicles.However, it presumes that the costs are well-understood at the time andlocation where the data is generated. To handle cases where unexpectedcongestion exists in some region or time period, one also needs togenerate dynamic priority functions. Here, the dynamic priority shouldapproximate the marginal utility that would be lost if a sample weredropped. One can compute this “remainder utility” by summing themarginal utility in the remaining cells:

${\hat{U}}_{i,j}^{R} = {\overset{p{(i)}}{\sum\limits_{k - j + 1}}{\hat{U}}_{i,j}}$

and then fitting a line to the (zj, Û_(i,j) ^(R)). This line determinesthe intercepts Uorigin and Udistance in the dynamic priority portion ofmicroutility i. The additional intercept, Utime, which governs theamount of value the microutility would loses due to a delay, is found byusing the same slope from Uorigin to Udistance, as is observed onaverage in the utility function U, that is:

$E_{f,d}\frac{\partial}{\partial_{\tau}}{{U\left( {f,\tau,d} \right)}.}$

FIG. 12 illustrates a set of microutilities, assuming evenly spacedzj=j. So for microutility 3, a line was fit to the remainders of row 3:(0, 200), (1, 135), (2, 80), (3, 35).

Finally the system completes the strategy generation process byestablishing a uniform pattern of microutility generation by choosing apermutation p, with p(1)=1 to insure that the first microutility is thestrongest microutility. The above strategy algorithm has the propertythat the furthest-traveling microutilities also cover the cells with thehighest marginal utilities, and so will have the highest dynamicpriority. It is also possible to generate a more randomized tiling ofthe Û_(i,j) array when fitting the dynamic priority portion of themicroutility.

To generate microutilities the system maintains an index k, into themicroutility strategy permutation p. When the application supplies a newdata sample, a microutility is generated with range D(p(k)) max andmicroutility given by U(p(k)) origin, U(p(k)) distance, and U(p(k)) timeas precomputed above; k is incremented modulo the set size forsubsequent microutility generation.

If an application's data samples change dramatically then an applicationcan reset the microutility generation. This is accomplished by settingk=1, thereby ensuring that the strongest microutility is the nextmicroutility sent.

Congestion Management

The forwarding sublayer is the central decision-making layer. Itreceives data updates, evaluates their microutilities, and uses thefollowing values to make decisions about further handling of the data.

If the data item is within the geometric domain of its microutility,then the data item is delivered to any local applications interested inthe data. This is accomplished by passing the data “up” the protocolstack shown in FIG. 2 to the application layer. In some implementations,it may be desirable to always deliver received data to the application,even if it is outside the geometric domain, since the cost of sendingthe data has already been incurred.

If the data item's microutility is less than or equal to a “dropthreshold” then the data is dropped or discarded. This is an importantreactive step that keeps the peer-to-peer network operating efficientlywhen local congestion is detected. If the data item's microutility isabove the drop threshold then it is broadcast in the local neighborhood.

On certain nodes, with long-range media capability, if the data item'sevaluated microutility is above a higher “premium threshold” then inaddition to broadcasting locally it is also transmitted longerdistances, using, for example, fixed wired or wireless infrastructure.

If the data will retain its value over time, then the data is storedlocally for potential future forwarding at a later time(store-and-forward).

All of the above decisions are guided by microutility. The immediatepropagation options are diagrammed in FIG. 13. FIG. 14 shows theprotocol stack with the additional role of the forwarding sublayerexpanded.

When congestion is detected the drop threshold is increased so that lessdata is forwarded, and using microutility means that it is the lessvaluable data that will be dropped. Data samples are evaluatedindividually, but the variations in microutility among data sourcesmeans that dropping will intelligently winnow streams of data samplesfrom the same source.

Referring to FIG. 15, assume that there is a node that becomesunexpectedly congested. The node establishes a threshold and discardsall data samples with priority less than the threshold. With high levelsof congestion, the congested node must establish a higher threshold toremove more traffic. In FIG. 15, this results in forwarding of only 30%of the traffic for data stream A (left bars), and none of the trafficfrom stream B (right bars). If there is low congestion, the lowthreshold would forward 60% of the traffic from stream A and 80% of thetraffic for stream B.

It must be noted that the above approach works more effectively than afixed priority approach, which would assign a high but fixed priority todata stream A. This would cause the node to solve all congestionproblems by discarding data from stream B, eventually completelysuppressing that stream. The microutilities vary from one microutilityto the next and can find better solutions. The result is a drop decisionthat is just as simple as those for fixed priorities, but achieves mostof the benefits of full utility functions with little cost or overhead.

While the microutilities determine which data is dropped, it is thelevel of the drop threshold that determines how much data is dropped.The drop threshold is set based on a measure of local congestion. Thiscontrol mechanism will be described later with reference to thecongestion monitoring sublayer.

The smooth functioning of a network often requires careful management ofload. This is important for carrier-sense wireless peer-to-peernetworks, since, carrier sense is never perfect, and high loads willcause unanticipated collisions will degrade throughput. In schedulednetworks, this can be even more critical, since there are only a fixednumber of slots available and it would be undesirable to fill themcompletely, leaving no space for late-arriving critical packets.

When managing traffic at a local node, it is important to considertraffic at neighboring nodes, which may have much higher loads. It hasbeen found that the local queue length at the MAC layer in the 802.11protocol stack is a good measure of congestion in the neighborhood. Evenif the local load is light, high levels of traffic at nodes within a fewhops will be detected by the carrier sense, causing the local queue tofill. The nodes may exchange information as part of data sample packets,an explicit management packet or out-of-band (i.e., not in a packet).This information may also be contained in a repository or database, aspart of the previously-provided traffic information. Further, exchangesbetween nodes may be authenticated or encrypted.

Moreover, if the system measures local queue length (QL), and controlsto keep local queue length short, then the system can make importantdrop decisions and gossip suppression decisions higher up in theprotocol stack, before the packet is released to the MAC layer, whereits eventual transmission is inflexibly determined by the lower-levelprotocol (beyond the control of the information layer) while it waits inthe MAC queue. At the Information Layer, in addition to the data samplebeing dropped or propagated, the data sample may be propagated afterbeing temporarily delayed.

The congestion measurement is used to control the forwarding sublayer.This is accomplished by varying the dropping threshold, which may beaccomplished by many mechanisms. For example, the system might use anexponential function of local queue length, proportional to QL̂1.5, toset the drop threshold. This allows the system to permit extremelyhigh-utility packets to temporarily raise the congestion level, butlimits the amount of that increase due to the rapid increase of thethreshold as congestion goes up. The actual increase in the threshold isset according to a calibration mechanism. This mechanism maintains ameasure of the mean and standard deviation of recently receivedmicroutilities, evaluated at the forwarding node's location at thecurrent time. Knowing this information allows the threshold to be setsuch that a specific amount of traffic can be dropped, within theaccuracy of the approximation of the calibration approach. Othercalibration techniques might include keeping more detailed statistics ofmicroutilities, histograms of received microutilities, or more detailedinformation about how microutilities are varying over time.

Other measures, such as local queue delay, and packet-size weightedqueue length are closely related to queue length, and may be moreaccurate measures of congestion. These are also easily measured by acongestion sublayer that can observe each packet as it is released tothe network stack, and can establish a monitor on each packet as it istransmitted from the MAC layer. Further, other measures of queue length,such as average queue length and aggregate queue length may also beused. Historic measures may also be used, such as historic packet-sizeweighted queue lengths, where recent packet-size weighted queue lengthshave a larger effect on the determined local data traffic level thanless recent queue lengths. This can be applied to any congestion measurementioned above.

The neighborhood congestion measurement can be further extended to a2-hop neighborhood by piggy-backing the local measurement of queuelength in the header of outgoing packets. This is easily accomplished bythe congestion monitoring sublayer at this location in the protocolstack: it inserts and recovers measurements from the packet headers, itdoes not otherwise modify the packets. The measurements from packetheaders of neighboring nodes may be combined with the measurements forthe local node to determine the local data traffic level.

It is possible to devise more precise control mechanisms than QL̂1.5 thatcan manage queue length to more consistently maintain a chosen queuelength. These methods would involve, for example, feedback mechanismsusing the difference between the current measured queue length and adesired queue length, taking advantage of traditional feedback controlsystem design and analysis techniques.

Similarly, while the criterion for dropping data samples is discussed asbeing a threshold, it is possible that other criteria may be used aswell. The local data traffic, which may or may not include neighboringnode traffic, is used to define a criterion for congestion. Thiscriterion is then applied to the microutility to determine which datasamples, if any, are to be dropped. For example, the microutilities ofrecently received data samples may be used to determine the criteria.These recently received microutilities may also be used to rescale themicroutility for a current data sample prior to applying the criteria tothat microutility.

Utility Guided Media-Agility

For the fixed nodes of the network (for example, stop lights, roadsigns, etc.) the system has included in the design a longer range fixedwireless capability such as WiMax.

Because such longer-range radio transmissions cover larger areas, theyconsume more spatial bandwidth (i.e. bandwidth-area). They are consuminga scarcer resource and hence a premium threshold is used to route onlythe most valuable data items through the long range media. Like the dropthreshold, the premium threshold is set based on a local measure ofcongestion in the long-range channel. Any of the measures of local datatraffic levels discussed above may be used to determine the mediathrough which data samples will be propagated.

The longer range medium will have its own lower level protocol stack,but will share the forwarding sublayer and broadcast sublayer as shownin FIG. 14. Even data items that have passed above the premiumthreshold, or have met some other criteria of which a threshold is oneexample, and are routed via the long range channel are still processedby the forwarding sublayer at the recipient. In particular, they must beabove the premium threshold, after each long range hop, to continuepropagating in the long range channel. Note that being above the premiumthreshold does not suppress simultaneous transmission of the data viathe normal vehicle-to-vehicle radio protocol. Information propagationoccurs via both methods, and any redundancies are prevented via thenormal broadcast sublayer mechanisms.

Additionally, the long-range and standard media may be associated withdifferent ranges of microutilities. For example, data samples below acertain threshold will be dropped. Data samples having microutilities ina particular range may be propagated in only the standard medium,microutilities in a different range may be propagated only in thepremium medium, with yet another range of microutilities beingpropagated in both media. Variations and combinations between ranges andthe channels are left up to the implementation for particular systems.In addition, since the evaluation of microutilities is based upon thecharacteristics of the node at which the evaluation is being performed,each relay node may repeat the above process of determining the mediafor transmission.

In addition to using the microutility to select the media, thecharacteristics of the media and the nodes connected by a particularmedium may determine the transmission of the data sample. Onecharacteristic already implied is that the media are characterized bythe nodes connected by each particular medium. This information may beexchanged between nodes connected to a particular medium, or may be theresult of nodes registering with a central repository that maintains thecontext of each node. The implementation of the exchange may depend uponthe medium. The subset of nodes connected to a particular medium may beonly direct peers, some strict subset of nodes or all nodes. This ‘nodecontext’ may change over time and updates may be propagated tointerested nodes, such as those still connected to that particularmedium.

In addition, the characteristics of the media used to guide theselection of the transmission media may include an evaluation of themicroutility at the remote end of a connection through the media. Forexample, in an infrastructure system a node could be connected via apoint-to-point link to a remote node. In this instance, there is noreason to send an update over the point-to-point link if it will havezero utility at the receiving node.

The characteristics of the media may depend upon the transmitting node,the receiving node, the nature of the data being transmitted and thecondition of the channels in the media. For example, In a long rangemicrowave system a node A may have two neighbors B and C. The link tothe near node B may support high-speed data transfer while the link onthe far node C may only support low-speed data transfer. The transferrate may also be chosen based on interference noise detected by thesystem at the time of transfer.

Other characteristics may also play a factor in the selection of themedia for transmission. The characteristics of the media may determinethe number of samples included in a packet, and determine encryption andsecurity requirements.

In addition, the management of updates, congestion and store and forwardimplementations may be affected by the selection of the media. Forexample, transmission of data samples through the media may becontrolled such that duplicate samples are detected and suppressed oneach media, unless a hold is in use. The suppression mechanism may bemedia-dependent, and hold-and-forward mechanisms may be independent fromhold-and-forward mechanisms used in other types of media.

Store-and-Forward

Store-and-forward addresses gaps that will naturally develop in thepeer-to-peer connectivity of the network. In urban areas, traffic lightswill bunch vehicles together around the traffic lights, but may leavegaps in the mid-block region. Furthermore, these bunches will oftenpersist as vehicles travel between intersections. In rural areas the lowdensities of vehicles will create transmission gaps.

FIG. 16 illustrates a scenario caused by traffic lights that havebunched the vehicles together. While data generated in group A 70 willpropagate rapidly to all vehicles in group A, the flooding of some dataitems within group A will be complete before vehicles in group A movewithin range of group B 72. This would result in those data items neverreaching group B. In these kinds of scenarios, coverage can be greatlyimproved by storing the updates briefly in the vehicles as they move,and passing them to other vehicles encountered later.

The challenge of store-and-forward techniques is to determine when astored update should be forwarded again. If vehicles simply periodicallyrebroadcast updates they are storing, then considerable additionaltraffic is generated and this extra traffic may be entirely unnecessary,for example, when the initial propagation has traveled through a wellconnected network and successfully reached all recipients. Even whenrebroadcasts are necessary, they may be necessary only at certain times,for example, when group A first encounters group B in the middle of FIG.16. Once a stored update is received by the new node, it is madeavailable to the application via the normal mechanism for received data.

Rather than periodically rebroadcasting updates, one may instead use asynopsis approach. A synopsis is a highly compressed representation ofthe contents of a node's stored updates, these stored updates beingreferred to here as a “hold.” In the synopsis each update being storedin hold is represented in abbreviated form, such as by a hash function.In one embodiment already implemented by the inventors, each update isrepresented by only 8 bits, which is a considerable savings over thesize of a full data sample, which can be 500-2000 bits or more. In thisembodiment, each node periodically piggybacks a synopsis on a regularoutgoing update message. Recipients of the synopsis compare it withtheir own stored update synopses in order to determine which updates aremissing in their neighbor's hold, and rebroadcast only those updates.

This is sometimes referred to as “anti-entropy” or “set reconciliationproblem.”Updates from the same data source will be time stamped, and inmost applications a node only wishes to retain the most recent updatewith the most recent time stamp from a source. This may be referred toas obsoleting semantics, and obsoleting semantics will be assumed indiscussion that follows, as it is the more difficult case to handle.However, the method described here can also support data types withnon-obsoleting semantics or a mix of obsoleting and non-obsoletingsemantics by retaining all updates from each source while they continueto have utility above some threshold, and considering them as separateentities for purposes of synopsis generation and comparison and hold andforward. In the case of obsoleting semantics, received data is onlyprovided to the application if it is more recent than the updatesalready provided to the application.

In one embodiment, it is possible to solve the set reconciliationproblem efficiently with a sorted-list synopsis. In such anarchitecture, microutilities give a natural way to sort the updates.Furthermore, by reconciling the highest microutility differences first,it ensures that scarce transmission resources are allocated to the mostvaluable rebroadcasts. Sorted updates allow efficient comparison ofsynopses with the local hold and simplify prioritization of updates.

A sorted-list synopsis is constructed by sorting the updates intomicroutility order and computing a short hash of the stream ID and thetimestamp for each update. This list of hashes is sent as a synopsis.FIG. 17 shows a synopsis of the data items in local storage beingcompared to a synopsis received from a remote sender.

The comparison of two synopses may be computed with a Levenshteindistance algorithm. The Levenshtein distance is the minimum number of“edits,” insertion or deletion of a character, that are necessary tomake two strings equal. In this application the Levenshtein distancecomputation identifies updates that differ between two different holds.

After comparison, the action taken depends on the results of thealignment. If the local node holds the first unmatched difference in thesort difference, as shown at 74 in FIG. 17, then the local nodetransmits a batch of differences from its storage. FIG. 17 shows 4differences being sent. The minimum and maximum number of differences tobe included in such a batch are parameters that can be set in thisalgorithm. If the remote synopsis holds the first (highest microutility)difference in the sort difference, as shown at 78 in FIG. 18, the localnode sends its synopsis by adding it to the next outgoing packet. Thissent synopsis will likely provoke the remote node, after it does asynopsis compare, to send differences back to the local node.

The reason for this asymmetry is that the when two nodes hold differentage updates from the same source, one would like the most recent updateto be reconciled between the two nodes. The most recent update will havethe highest microutility, so it is more probable that the firstdifference will be sent in the right direction. The protocol sends abatch of differences, so this will not necessarily be true of the secondand subsequent differences in a batch. Batches are sent for efficiency;it is still likely that subsequent differences in a batch will also bebeneficial to the recipient.

Updates sent from hold in response to a synopsis mismatch are broadcaston the appropriate media, just like regular updates being forwarded.Because of the broadcast nature of the updates, they may reconcilemultiple holds at the same time in the 1-hop neighborhood. Due to thequeuing and delays of packets already released to the MAC layer, a nodemay receive a stale synopsis from a neighbor after it has alreadyre-broadcast an update from hold. To avoid multiple unnecessaryre-transmissions, a node will place an update in a brief hold-down aftersending it from hold. This avoids multiple re-transmissions due to stalesynopses.

The summarized updates contained in the synopsis are so compressed thatthey may occasionally “collide” accidentally, that is, they will matchwhen they should not match: two different data items will match in thecomparison algorithm. However, this failure should be rare. Theconsequence of these failures is that a small amount of unnecessarytraffic will be transmitted, but it has no effect in terms ofcorrectness or freshness of the data in the hold.

The updates stored in hold may be kept sorted with any sortingalgorithm. Shell sort has been found to be especially effective, becauseit is very efficient when the lists are already almost correctly sorted.This will often be the case, since the microutilities change very littlebetween sorts. Moreover, the system only reevaluates the microutilitiesat a limited number of locations on a regular grid, that is, itquantizes the space and time coordinates of vehicles to a regular grid.This limits the number of times hold needs to be sorted, and increasesthe likelihood that neighboring nodes will have the same sorted order.The system evaluates the dynamic priorities using these quantizedparameters. It is possible that the node transmitting the synopsis mayinclude the quantized parameters with the synopsis, so the receivingnode can use the quantized parameters to compare the received synopsiswith the local synopsis. The local node can also use the quantizedparameters to sort the local storage.

The alignment, such as Levenshtein distance, uses dynamic programming tofind the alignment with the fewest unmatched items, it normally takesO(n̂2) time and space, where n is the length of the lists begin compared.This embodiment has modified the traditional algorithm so it finds thefirst small number of differences between the two lists. In one possibleimplementation, the traditional Levenshtein distance algorithmsystematically computes a n by n matrix where the (i,j) entry representsthe best alignment between the first characters in one string and thefirst j characters in another string. Entries far off the diagonal, thatis, where |i−j|>k, must have distance>k, since at least k edits arenecessary to make these substrings have the same length. An algorithm,such as our synopsis comparison algorithm, that is searching for a smallnumber of differences between two lists does not need to compute any ofthe entries far off the diagonal, and so can compute the distance moreefficiently in O(k n) time and space.

The store-and-forward operation includes several rate-limitingmechanisms to ensure that synopses are not sent too frequently andupdates are not rebroadcast too frequently. For example, one way to ratelimit the frequency that synopses are sent is to record when the lastsynopsis was sent, and not send a new synopses until small fixed periodof time has elapsed. There is, however, one important limiting mechanismcalled Limbo.

Limbo governs the store-and-forward operation. When an update is firstgenerated and initially propagated through the network, it will normallybe placed in the hold of all recipient nodes. The system does not wishto have store-and-forward operations begin immediately, in order toavoid sending samples which might be received via normal peer-to-peertransmission mechanisms, so a delay time, which will be referred to aslimbo, is applied to all recently generated updates. If the elapsed timesince data generation is less than the limbo time, then for purposes ofstore-and-forward operation, the microutility is assumed to be zero andthese recently generated updates are sorted to the bottom of a node'shold and will not appear in the synopses generated. FIG. 19 shows thelimbo line on the time-space domain of the microutility.

Limbo may be defined as a fixed time relative to the time the data wasgenerated. Limbo prevents store-and-forward operations from beginningtoo quickly. It allows the initial propagation of the update to spreadto all nodes within good multihop connectivity. During initialpropagation there are many brief inconsistencies that will be resolvedalmost immediately. Premature store-and-forward synopses comparison ofthese updates would respond to many temporary inconsistencies ratherthan fixing more serious longstanding inconsistencies. Limbo reservesstore-and-forward operations for older updates, which have already had achance to be exchanged among nearby nodes.

While the criteria for limbo used here is related to a time the data wasgenerated, one skilled in the art knows that limbo can be any number ofcriteria that temporarily prevent store and forward operations to avoidaddressing inconsistencies that will resolve themselves in a shortperiod of time.

Limbo also keeps updates from well-connected nodes from everparticipating in store-and-forward operations. In the well-connectedsituation, before an update can leave limbo and begin participating instore-and-forward, it will be replaced in limbo with another update fromthe same source with even less elapsed time, as per obsoletingsemantics. Updates will only leave limbo when connectivity gaps developand the source becomes disconnected from its intended recipients.

It will be appreciated that various of the above-disclosed and otherfeatures and functions, or alternatives thereof, may be desirablycombined into many other different systems or applications. Also thatvarious presently unforeseen or unanticipated alternatives,modifications, variations, or improvements therein may be subsequentlymade by those skilled in the art which are also intended to beencompassed by the following claims.

1. A method of managing traffic in an ad hoc network, comprising: measuring local data traffic levels on at least one network resource; establishing criteria for different transmission media; evaluating a microutility of a data sample; and transmitting the data sample through one or more media, wherein the selection of the media is based upon evaluation of the microutility against the criteria.
 2. The method of claim 1, wherein the criteria are ranges of microutility values.
 3. The method of claim 1, wherein establishing criteria further comprises using the microutilites of recently received data samples to establish the criteria.
 4. The method of claim 2, wherein establishing criteria further comprises establishing one or more media to use for data samples having microutilities in a premium range.
 5. The method of claim 1, measuring local data traffic levels further comprising measuring queue length at the network resource.
 6. The method of claim 1, wherein measuring local data traffic levels further comprises measuring historic queue lengths where recent queue lengths have a larger effect than less recent queue lengths on a resulting measure.
 7. The method of claim 1, wherein measuring local data traffic levels further comprises measuring a local queue delay.
 8. The method of claim 1, wherein measuring local data traffic levels further comprises measuring historic local queue delay, where recent queue delays have a larger effect than less recent queue delays on a resulting measurement.
 9. The method of claim 1, wherein determining local data traffic levels further comprises measuring packet-size weighted queue length.
 10. The method of claim 1, wherein determining local data traffic levels further comprises measuring historic packet-size weighted queue lengths, where recent packet-size queue lengths have a larger effect than less recent packet-size queue lengths on a resulting measure.
 11. The method of claim 1, wherein transmitting the data sample through one or more media is repeated at each of an in transit forwarding node.
 12. The method of claim 1, the method further comprising storing the data sample if the data sample retains an information value over time.
 13. The method of claim 1, the method further comprising dropping the data sample if the microutility of the data sample does not fall into any range of microutility.
 14. The method of claim 1, wherein the selection of the media is based upon characteristics of the media.
 15. The method of claim 14, wherein the characteristics of the media are determined by nodes connected by a particular medium.
 16. The method of claim 14, wherein the characteristics of the media are determined by registration of nodes and media to which the nodes are connected with a central repository.
 17. The method of claim 14, wherein the characteristics of the media change over time and changes are propagated to interested nodes.
 18. The method of claim 15, wherein the nodes connected by the particular medium change over time.
 19. The method of claim 14, wherein the selection of the media is based upon the evaluation of the microutility at a remote end of a connection in the media.
 20. The method of claim 14, wherein the characteristics of the media is based upon a transmitting node, a receiving node, a nature of the data sample and conditions of channels in the media.
 21. The method of claim 1, the method further comprising controlling transmission of data samples through the media such that duplicate samples are detected and suppressed on each media, unless a hold is in use.
 22. The method of claim 21, controlling transmission further comprises using a media-dependent suppression mechanism.
 23. The method of claim 1, wherein each available medium, if employing a hold-and-forward mechanism, has a hold-and-forward mechanism independent from other media.
 24. The method of claim 14, wherein the characteristics of the media determines a number of samples included in a packet.
 25. The method of claim 14, wherein the characteristics of the media include encryption.
 26. The method of claim 14, wherein the characteristics of the media include security requirements.
 27. A device, comprising: a processor to: measure local data traffic levels on at least one network resource; establish criteria for different transmission media; evaluate a microutility of a data sample; and a port to allow the device to transmit the data sample through one or more media, wherein the selection of the media is based upon evaluation of the microutility against the criteria.
 27. The device of claim 26, wherein the processor to establish criteria further comprises the processor to use the microutilites of recently received data samples to establish the criteria.
 28. The device of claim 26, wherein the processor to measure local data traffic levels further comprise a processor to measure one of: queue length at the network resource, historic queue lengths where recent queue lengths have a larger effect than less recent queue lengths on a resulting measure, a local queue delay, historic local queue delay, where recent queue delays have a larger effect than less recent queue delays on a resulting measurement, packet-size weighted queue length, and historic packet-size weighted queue lengths, where recent packet-size queue lengths have a larger effect than less recent packet-size queue lengths on a resulting measure.
 29. The device of claim 26, the device further comprising a storage for storing the data sample if the data sample retains an information value over time.
 30. An article of computer-readable media containing instructions that, when executed, cause the computer to: measure local data traffic levels on at least one network resource; establish criteria for different transmission media; evaluate a microutility of a data sample; and transmit the data sample through one or more media, wherein the selection of the media is based upon evaluation of the microutility against the criteria.
 31. The article of claim 30, wherein the instructions that cause the computer to establish criteria further causes the computer to use the microutilites of recently received data samples to establish the criteria.
 32. The article of claim 30, wherein the instructions that cause the computer to transmit the data sample further cause the computer to select the media based upon characteristics of the media. 